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| Presentation | 2022-09-15 10:00 One-cut Network Pruning at Initialization with Explainable Image Concepts Yinan Yang (Rits Univ.), Ying Ji (Nagoya Univ.), Yu Wang (Hitotsubashi Univ.), Jien Kato (Rits Univ.)  PRMU2022-18 |
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| Abstract | (in Japanese) | (See Japanese page) |
| (in English) | Recent research investigates the feasibility of one-cut network pruning at initialization (OPAI). SNIP and GraSP are two representative OPAI methods. Recent work asserts SNIP and GraSP are data-independent as they are robust with random corrupting labels or pixels. It indicates that OPAI ``hardly exploits any information from training data''. However, we obtain an opposite conclusion by proposing Concept-based One-cut Network Pruning (COP) and Super Stitching sampling strategy. COP and Super Stitching extract high-level visual concepts from the dataset with ranking and then prunes the network by these concepts. We find that: (1) The COP and ablation experiments reveal that the OPAI is data dependent. (2) Super Stitching performs better than the original OPAI method on benchmark ImageNet using concepts rather than random images. COP and Super Stitching, in contrast to existing human-unfriendly pruning methods, employ genuine explainable visual concepts rather than blind pruning. | |
| Keyword | (in Japanese) | (See Japanese page) |
| (in English) | Network Pruning / Discriminative / Explainable Image Concepts / / / / / | |
| Reference Info. | IEICE Tech. Rep., vol. 122, no. 181, PRMU2022-18, pp. 49-54, Sept. 2022. | |
| Paper # | PRMU2022-18 | |
| Date of Issue | 2022-09-07 (PRMU) | |
| ISSN | Online edition: ISSN 2432-6380 | |
| Copyright and reproduction |
All rights are reserved and no part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopy, recording, or any information storage and retrieval system, without permission in writing from the publisher. Notwithstanding, instructors are permitted to photocopy isolated articles for noncommercial classroom use without fee. (License No.: 10GA0019/12GB0052/13GB0056/17GB0034/18GB0034) | |
| Download PDF | PRMU2022-18 |
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| Conference Information | |
| Committee | PRMU |
| Conference Date | 2022-09-14 - 2022-09-15 |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | Deep generative model |
| Paper Information | |
| Registration To | PRMU |
| Conference Code | 2022-09-PRMU |
| Language | English |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | One-cut Network Pruning at Initialization with Explainable Image Concepts |
| Sub Title (in English) | |
| Keyword(1) | Network Pruning |
| Keyword(2) | Discriminative |
| Keyword(3) | Explainable Image Concepts |
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| 1st Author's Name | Yinan Yang |
| 1st Author's Affiliation | Ritsumeikan University (Rits Univ.) |
| 2nd Author's Name | Ying Ji |
| 2nd Author's Affiliation | Nagoya University (Nagoya Univ.) |
| 3rd Author's Name | Yu Wang |
| 3rd Author's Affiliation | Hitotsubashi University (Hitotsubashi Univ.) |
| 4th Author's Name | Jien Kato |
| 4th Author's Affiliation | Ritsumeikan University (Rits Univ.) |
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| Speaker | Author-1 |
| Date Time | 2022-09-15 10:00:00 |
| Presentation Time | 15 minutes |
| Registration for | PRMU |
| Paper # | PRMU2022-18 |
| Volume (vol) | vol.122 |
| Number (no) | no.181 |
| Page | pp.49-54 |
| #Pages | 6 |
| Date of Issue | 2022-09-07 (PRMU) |
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